LIBSVM

LIBSVM and LIBLINEAR are two popular open source machine learning libraries, both developed at the National Taiwan University and both written in C++ though with a C API. LIBSVM implements the sequential minimal optimization (SMO) algorithm for kernelized support vector machines (SVMs), supporting classification and regression.[1] LIBLINEAR implements linear SVMs and logistic regression models trained using a coordinate descent algorithm.[2]

LIBSVM
Developer(s)Chih-Chung Chang and Chih-Jen Lin
Stable release
3.3 / August 11, 2022 (2022-08-11)
Repository
Written inJava, C++
Operating systemCross-platform
TypeMachine Learning
LicenseBSD
Websitewww.csie.ntu.edu.tw/~cjlin/libsvm

The SVM learning code from both libraries is often reused in other open source machine learning toolkits, including GATE, KNIME, Orange[3] and scikit-learn.[4] Bindings and ports exist for programming languages such as Java, MATLAB, R, Julia, and Python. It is available in e1071 library in R and scikit-learn in Python.

Both libraries are free software released under the 3-clause BSD license.[5][6]

References
  1. Chang, Chih-Chung; Lin, Chih-Jen (2011). "LIBSVM: A library for support vector machines". ACM Transactions on Intelligent Systems and Technology. 2 (3). doi:10.1145/1961189.1961199. S2CID 961425.
  2. R.-E. Fan; K.-W. Chang; C.-J. Hsieh; X.-R. Wang; C.-J. Lin (2008). "LIBLINEAR: A Library for Large Linear Classification". Journal of Machine Learning Research. 9: 1871–1874.
  3. Janez Demšar; Tomaž Curk; Aleš Erjavec; Črt Gorup; Tomaž Hočevar; Mitar Milutinovič; Martin Možina; Matija Polajnar; Marko Toplak; Anže Starič; Miha Stajdohar; Lan Umek; Lan Žagar; Jure Žbontar; Marinka Žitnik; Blaž Zupan (2013). "Orange: data mining toolbox in Python" (PDF). Journal of Machine Learning Research. 14 (1): 2349–2353.
  4. scikit-learn developers. "1.4. Support Vector Machines". scikit-learn.org. Retrieved 12 May 2022.
  5. "COPYRIGHT". LIBSVM. National Taiwan University.
  6. "COPYRIGHT". LIBLINEAR. National Taiwan University.


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